Which got me thinking and philosophizing…. Why is success [in anything] so rare? Why is failure [in everything] so common? Can “the odds” be predictably beaten? Etc.

I’ve studied achievement, success, and leadership of high performance teams since 1975. I’ve experienced my own personal and professional trials, tribulations, failures, and successes. And, in the case of Isogen International, Inc. (now Innodata Isogen), I had collected and built an unparalleled high-performance team of conceptual, technical, and project management specialists, who for 15 years have consistently delivered predictable and odds-defying success to clients.

So … I may possess an idea (or two) about the particulars of success and failure in general, and in particular, success and failure related to publishing processes, document production, knowledge management, etc. In December 2005, I particpated in a symposium where a research paper reported that failure rates were 77% in this genre of project. We all thought that was a great improvement over previously reported and observed failure statistics, until we heard the fine print:

1. Failures were defined as projects that did not complete.
2. Successes included projects wherein:
   • Projects were stopped and restarted, with or without scope changes.
   • Features were delivered at or above 50% of original specification.
   • Final cost was at or below 200% of original budget.

By the way, the other old codgers who were in attendance all agreed that success would be defined as (1) specified features (2) on time and (3) within budget. We explicitly operate in the realm where pre-defined features, budgets, and timelines are essential.

Without the fixed boundaries of the paying field and without the enforced rules of the game, no definition of winner or loser, and therefore no definition of success and failure, has meaning or importance. So the first essential unnatural act is to set the boundaries and the rules of the game, inviting in objectivity and empiricism. Eliminating self-serving bias is unnatural, yet it is essential for success.

Only then can we get to playing the game and improving our play. More on that later.

Everyone I talked with reminded me how much fun we had, and I’ve got to admit that, for many of us, Endicott in the late ’60’s had its similarities to Point Place from That ’70’s Show. (Use your imagination!) Among the people I ran with, most had moved and few attended the reunion. By the time you hit 35 years away, it seems that neither competitiveness nor curiosity are much of a draw anymore.

I still have family there, and a lot of relaxing things to do, not to mention the good local cuisine (really!).

We flew into Tri Cities Airport, situated between En Joie Golf Course and the Susquehanna River. For you other pilots, Goodrich Aviation is the first rate FBO there, now and for the past few years. It’s worth stopping in to see Doug Goodrich.

Tri Cities and many other airports in the Southern Tier of New York are hotbeds of glider activity. I am convinced that I am a pilot because of my childhood years of watching gliders loiter in the summer sky above, from my back yard.

More later. Please come back soon.

Knowledge is a collection of content, contexts, and relationships that expresses topical meaning with sufficient fidelity to meet the consumption needs of a user and/or the processing needs of an agent.

Now we get to talk cores. By considering some of the most essential characteristics of knowledge assets, we address and begin to solve some of most vexing issues of creating and sustaining knowledge economies.

Contexts for the Applicable Meaning of Core

Concepts of core are extant in diverse technical and scientific disciplines. Each offers some insights into how and why cores are essential to knowledge economies. Here are a few of the most striking contextual concepts of core:

• In Archaeology – A Core is a distinctive artifact that results from a specific investigative process.
• In In Biology – The Core (of fruits) is the place where seeds are.
• In Computing – Core usually means primary storage regardless of platform or technology.
• In Economics – A Core is the set of allocations in an economy that cannot be improved upon by subset of the economy (analogous to a Nash equilibrium).
• In Education – Core is equated to essential (as in curriculum).
• In Executive Management – Core assets are the critical revenue-producing assets of people, property, and technology. (per IAFSB, Asset is defined as A resource controlled by the enterprise as a result of past events, from which future economic benefits are expected to flow to the enterprise.)
• In Mathematics – The Core is the innermost set of objects in a group, usually invariant in their properties.
• In Nuclear Physics – The Core is the portion of a reactor containing the fuel.
• In Planetary Science – The Core is the densest innermost layer.

Tying these all together, knowledge cores can solve problems, generate revenue, and increase in their asset value, provided they are designed, engineered, and built to embody these (among other) characteristics:

1. Knowledge cores consist of knowledge components akin to seeds — previously elaborated as content, contexts, & relationships — all of which must be equivalent in their ability to be independently defined, created, identified, tracked, and used.
2. Each component in a knowledge core must be distinct in its properties, which are inferred from specific investigations into the properties needed to support consumers and agents. The properties of components and cores must be maintained as independent from and immutable by any other systems. All other such systems must be considered mutable and dependent on the knowledge assets.
3. Each component in a knowledge core must be continuously maintained at the highest fidelity possible. Dissonant material must be brought into consonance early, frequently, and completely to maintain fidelity. Arbitrary or regular opportunities to increase fidelity equate to asset enhancement, rather than equating to increased expense.
4. Each knowledge core must be considered as the primary source of raw material from which all other derivative products are generated via automated agent. Hand-crafting of derivative products (web pages, text, graphics, documents, RSS feeds, etc.) is deprecated in favor of an automated approach.

Now I think we have established some beginning footing for a third foundational pillar of knowledge management – Build and use knowledge components within knowledge cores to drive more revenue per unit of asset. I’ll elaborate later.

Come back soon and often …

A well-constructed technical definition accurately reflects different layers of understanding. (Contrasted to the hackneyed jargon often intended to obscure understanding.)

So as far as background assumptions about what people know and how they know it, here’s what I’ve been thinking (with others) for a few years:

• There are subjects that can be personally explicitly defined, or merely implicitly understood by a person;
• These subjects can be given one or more names;
• These subjects exist or occur within situations (or contexts);
• These contexts may or may not affect the meaning of a particular subject;
• These contexts can be personally explicitly defined, or merely implicitly understood;
• These contexts can be named or not;
• Any subject may become related to any other subject in ways that may or may not be known in advance;
• Any contexts may become related to any other contexts in ways that may or may not be known in advance;
• Any relationship may be personally explicitly defined, or merely implicitly understood by a person;
• These relationships can be named or not;
• Any artifact that comprises subjects, contexts, or relationships is most commonly expressed in ordinary words, phrases, or symbols that have meaning to a person. That is, they are at the content level of expressiveness more often than they are data objects.

My Working Definition of Knowledge

A collection of content, contexts, and relationships that expresses topical meaning with sufficient fidelity to meet the consumption needs of a user and/or the processing needs of an agent.

Come back soon, for more …

The most recent of the many events, projects, and leadership situations from which I’ve guided commercial publishers was New York University’s Center for Publishing symposium ”Future Tense – Emerging Trends in Publishing Workflow Management”. The symposium was attended by a collection of world renowned experts in publishing process and workflow. Some of us presented and taught, some of us watched and learned, I think I even saw one of us taking a snooze! So it was a mixed crowd, tied together only by “commercial publishing expertise”.

One thing everybody accepts, “If you keep on doing what you have been doing up to now, nothing can help you achieve better results.” In the face of empirical evidence to the contrary, traditional and conventional wisdom must evolve. Whether it be one or more of the process steps: product design; authoring; content acquisition; aggregation; editorial practices; fabrication processes; indexing; abstracting; production; preflight; composition; distribution; or the interfaces that exist that tie them together … stop doing what you’ve always done, consider doing what others who get better results have learned to do, and therefore gain the opportunity to reduce future harm. Not really a popular message, but true nonetheless.

Come back for more …

XML markup is meta-data which is topical, explicit, specific, precise, and unambiguous, used to describe document content. Typographical clues are meta-data which is visual, implicit, general, inexact, and ambiguous, used to describe document content. During document production, applying XML markup rather than applying typographical clues becomes the content-organizing value-add of the author or editor. In either case, with regards to the document content, whatever the author/editor “knows and expresses” becomes impersonally deliverable to a consumer who “needs to know”.

Here’s where documents relate to knowledge management, knowledge ecologies, and knowledge economies…

When intended so to do, the documentary content marked up in XML (topical, explicit, specific, precise, and unambiguous) becomes more efficient for knowledge storage, knowledge processing, and knowledge transfer than any particular document which may encapsulates it from time to time. Each topical content object within the document has been made more individually accessible, is more individually self-descriptive, has been elevated in individual importance, and ultimately it individually costs more to produce.

The “first-use document” (as a contextual wrapper for a collection of expensive topical objects) normally costs more than a document which has been encoded typographically (visual, implicit, general, inexact, and ambiguous). But, each subsequent document costs remarkably less, while it retains the complete utility of each topical content object it encapsulates.

The first pillar of knowledge management — primum non nocere — First, do no harm: Start to eliminate future harm by reducing spending on non-topical, non-explicit, non-specific, imprecise, and ambiguous documents, and replacing it with increasing investment in topical, explicit, specific, precise, and unambiguous essential content objects.

Come back soon for more pillars.